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. 2021 Aug 24;8(1):223.
doi: 10.1038/s41597-021-01002-w.

Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis

Bastian Schulze  1 , Denice van Herwerden  2 , Ian Allan  3 , Lubertus Bijlsma  4 , Nestor Etxebarria  5 , Martin Hansen  6 , Sylvain Merel  7 , Branislav Vrana  8 , Reza Aalizadeh  9 , Bernard Bajema  10 , Florian Dubocq  11 , Gianluca Coppola  12 , Aurélie Fildier  13 , Pavla Fialová  8 , Emil Frøkjær  6 , Roman Grabic  14 , Pablo Gago-Ferrero  15 , Thorsten Gravert  6 , Juliane Hollender  16 , Nina Huynh  17 , Griet Jacobs  18 , Tim Jonkers  19 , Sarit Kaserzon  1 , Marja Lamoree  19 , Julien Le Roux  17 , Teresa Mairinger  16   20 , Christelle Margoum  7 , Giuseppe Mascolo  21 , Emmanuelle Mebold  22 , Frank Menger  23 , Cécile Miège  7 , Jeroen Meijer  19 , Régis Moilleron  17 , Sapia Murgolo  21 , Massimo Peruzzo  13 , Martijn Pijnappels  24 , Malcolm Reid  3 , Claudio Roscioli  25 , Coralie Soulier  26 , Sara Valsecchi  25 , Nikolaos Thomaidis  9 , Emmanuelle Vulliet  11 , Robert Young  6   27 , Saer Samanipour  28   29
Affiliations

Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis

Bastian Schulze et al. Sci Data. .

Abstract

Non-target analysis (NTA) employing high-resolution mass spectrometry is a commonly applied approach for the detection of novel chemicals of emerging concern in complex environmental samples. NTA typically results in large and information-rich datasets that require computer aided (ideally automated) strategies for their processing and interpretation. Such strategies do however raise the challenge of reproducibility between and within different processing workflows. An effective strategy to mitigate such problems is the implementation of inter-laboratory studies (ILS) with the aim to evaluate different workflows and agree on harmonized/standardized quality control procedures. Here we present the data generated during such an ILS. This study was organized through the Norman Network and included 21 participants from 11 countries. A set of samples based on the passive sampling of drinking water pre and post treatment was shipped to all the participating laboratories for analysis, using one pre-defined method and one locally (i.e. in-house) developed method. The data generated represents a valuable resource (i.e. benchmark) for future developments of algorithms and workflows for NTA experiments.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Map depicting the distribution of participating laboratories in the international ILS. The size of the bubble represents the number of participating labs in each country and the two organizing institutes of the ILS are represented by the red location markers. All laboratories providing data are located in Europe (a) except for one which is located in Australia (b).
Fig. 2
Fig. 2
The experimental design of the ILS.
Fig. 3
Fig. 3
The detection overview for all six ISs for both pre-defined (dark gray) and in-house (light purple) methods. The white boxes are non-detected ISs.
Fig. 4
Fig. 4
The detection frequency of each IS in pre-defined and in-house method.
Fig. 5
Fig. 5
Variance observed in (a) the intensity in log scale, (b) the mass error (mDa), and (c) the retention factor of each IS recorded via pre-defined method. The retention factor was obtained by dividing the retention time for each detected IS by the total analysis time.
Fig. 6
Fig. 6
Variance observed in (a) the intensity in log scale, (b) the mass error (mDa), and (c) the retention factor of each IS recorded via in-house method. The retention factor was obtained by dividing the retention time for each detected IS by the total analysis time.
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